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LLM Token & Credit Optimization

Benchmark prompt compression, semantic response caching, speculative decoding, and grammar-constrained decoding

1. PROMPT BUFFERTokens: 1,250Hello customer team...Hope you are having...[KEEP] INV-9082 Error[KEEP] Visa 4921 HTTP500TTFT: 450 ms TTFT2. LLMLINGUAEntropy PruningRate: -55.2%Loss: 0.05 (99.5% Acc)3. HNSW CACHECosine SimilaritySim: 0.720 (MISS)Pass to LLM4. SPECULATIVE (K=5)Draft 1B → Target 70B✓✓✓✗-Speedup: 2.80x (Accept 4/5)5. GBNF LOGIT MASKValid Vocabulary MaskMasked -∞OKJSON Schema 100% Valid6. TELEMETRYTTFT Latency:450 ms TTFTEst. Request Cost:$0.00375 / reqCache & Speculative:Disabled (1x)Savings: -84.2% API Cost
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High-Cost Prompt Ingestion

Raw user prompts often contain polite greetings, boilerplate intros, and repetitive doc snippets. Passing all 1,250 raw tokens directly to the cloud LLM consumes full input token charges ($3.00/1M) and incurs high prefill latency (TTFT = 450ms).

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